// CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>
    // CHECK: %[[CONCAT:.*]] = "tfl.concatenation"(%arg0, %arg1) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xf32>, tensor<1xf32>) -> tensor<2xf32>
    // CHECK: %[[VAL_1:.*]] = "tfl.reshape"(%[[CONCAT]], %[[VAL_0]]) : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
    // CHECK: return %[[VAL_1]]
  }

  func.return %1 : tensor<1x1x257xi32>
  // CHECK: %[[CONST_DIM:.*]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[RED:.*]] = "tfl.sum"(%arg0, %[[CONST_DIM]]) <{keep_dims = true}> : (tensor<1x16384x257xi32>, tensor<1xi32>) -> tensor<1x1x257xi32>
}

// CHECK-LABEL: BatchmatmulToReduceSumF32
// CHECK-NOT: "tfl.batch_matmul"

  %q_bias = "quantfork.qcast"(%bias) : (tensor<2xf32>) -> tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>
  %dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>

      %c = stablehlo.constant dense<40> : tensor<2xi32>
      %0 = "tf.UnconvertedOp"(%arg0, %c) {align_corners = false, half_pixel_centers = false} : (tensor<1x20x20x28xf32>, tensor<2xi32>) -> tensor<1x40x40x28xf32>
      func.return %0 : tensor<1x40x40x28xf32>
  }

  %perm = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %perm1 = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
  %1 = "tfl.transpose"(%arg1, %perm1) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %2 = tfl.add %0, %1 { fused_activation_function = "NONE" } : tensor<5x2x3x4xf32>

  // CHECK-DAG: %[[RHS_1:.*]] = mhlo.convert %arg1 : tensor<2xi32>
  // CHECK-DAG: %[[RHS_2:.*]] = mhlo.bitcast_convert %[[RHS_1]] : (tensor<2xi32>) -> tensor<2x!quant.uniform<i32:f32, 2.000000e+00:4>>
  %input1_qint = "tf.Cast"(%input1) {Truncate = false} : (tensor<3x2xi32>) -> tensor<3x2x!tf_type.qint32>
  %input2_qint = "tf.Cast"(%input2) {Truncate = false} : (tensor<2xi32>) -> tensor<2x!tf_type.qint32>

  %multiples = "tf.Const"() { device = "/device:CPU:0", value = dense<[7,3]> : tensor<2xi32> } : () -> tensor<2xi32>
  // CHECK: T = !corert.string
  %output = "tf.Tile"(%arg, %multiples) { device = "/device:CPU:0" } : (tensor<1x2x!tf_type.string>, tensor<2xi32>) -> tensor<?x6x!tf_type.string>
  func.return %output : tensor<?x6x!tf_type.string>
}

// CHECK-LABEL: func @shape
func.func @shape() {

# CHECK-DAG:       %[[VAL_2:.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
# CHECK-DAG:       %[[VAL_3:.*]] = "tfl.no_value"() <{value}> : () -> none
# CHECK-DAG:       %[[VAL_6:.*]] = arith.constant dense<0> : tensor<i32>

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module {
      "tf_saved_model.global_tensor"() {type = tensor<2xi32>, value = dense<2> : tensor<2xi32>, sym_name = "__tf_saved_model_x"} : () -> ()
      func.func @main(%arg_0: tensor<!tf_type.resource<tensor<2xi32>>> {tf_saved_model.bound_input = @__tf_saved_model_x}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }

// -----

// CHECK-LABEL: func_with_tfl_attrs
func.func @func_with_tfl_attrs(%arg0: tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> attributes {tf.entry_function = {inputs = "arg0,arg1", outputs = "arg0"}} {
  return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

// -----

// There are cases where ODML converter relies on constants not being folded or